High Throughput Screening for Antimicrobial Dosing Regimens

ABSTRACT

Provided herein are methods and computer-implemented systems for using computer simulations to predict likelihood of a cell population associated with a pathophysiological condition acquiring resistance to a therapeutic agent, to screen for therapeutic agents effective to suppress acquisition of resistance within a cell population and to treat the pathophysiological conditions associated therewith. The computer simulation comprises at least an input/out system and a mathematical model, including operably linked equations, parameter values and constant values, of growth response over a period of tune of a cell population in contact with an therapeutic agent.

CROSS-REFERENCE TO RELATED APPLICATION

This nonprovisional application claims benefit of priority of provisional application U.S. Ser. No. 60/718,463, filed Sep. 19, 2005, now abandoned.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to the fields of microbial pathology and acquired resistance of pathogens and mathematical modeling. More specifically, the present invention provides a computer-implemented method to predict acquisition of resistance within a microbial population to an antimicrobial agent and a screening tool to predict efficacy of potential antimicrobial agents in preventing acquisition of resistance within microbial populations.

2. Description of the Related Art

Resistance to antimicrobial agents is a serious problem that renders the rapid development of new agents an urgent priority. The alarming spread of antimicrobial resistance is threatening the therapeutic armamentarium (1-11). It has been estimated that nearly 2 million people in the U.S. acquire bacterial infections while in the hospital and about 90,000 of them die every year. The total cost of antimicrobial resistance to U.S. society is nearly $5 billion annually. It is likely that effective treatment may not be available for many common infections in the not too distant future and the risk of going back to the pre-antibiotic era in the event of an outbreak is present (4). Broad-spectrum antimicrobial resistance in HIV, tuberculosis, Gram-negative bacteria, e.g., Pseudomonas aeruginosa, Acinetobacter baumannii, etc., and agents implicated in bioterrorism, e.g., Bacillus anthracis, is especially worrisome and has world-wide implications. It is therefore imperative that new and effective antimicrobial agents are developed rapidly to keep up with our combat against infections caused by these pathogens.

As widely appreciated as the magnitude of this problem may be, the traditional approach to the development of antimicrobial agents is unlikely to meet this critical need. The traditional approach has focused on the identification of new metabolic targets and agents to interfere with essential pathways. Relatively little attention has been paid to the impact of the dosing regimen, i.e., dose and dosing frequency, of an active agent on the emergence of resistance.

In-vitro and in-vivo experimental data demonstrate that dosing regimen may play an important role in the development of resistance; sub-optimal dosing regimens represent a selective pressure that facilitates resistance development, whereas using optimal dosing regimens may suppress the emergence of resistance (2-3,12). However, multiple modifiable factors, e.g., the total daily dose, dosing frequency, length of (intravenous) administration and duration of therapy, etc., are involved in rational design of dosing regimens. Each factor may have a significant impact on the killing activity and propensity to suppress resistance emergence, depending on the pharmacodynamic properties of the agents and clinically achievable concentrations associated with acceptable toxicity.

The numerous combinations of these variables involved in designing dosing regimens are prohibitory for comprehensive laboratory or clinical evaluation of all the different scenarios. In view of the labor-intensiveness of each investigation, several regimens are often empirically chosen to be studied. This approach is poorly guided and may lead to prematurely abandoning the development of good agent candidates. As a result, the potentials of new agents may not be thoroughly realized.

Pharmacodynamic modeling has been used as a decision support tool to facilitate rational dosage design. It emphasizes the fact that effective antimicrobial treatment is attributed to neither antimicrobial agent potency (exposure) nor pathogen susceptibility alone, but rather a complex interplay of both factors. In spite of that, conventional modeling methods may be overly simplistic, relying on surrogate pharmacodynamic indices, e.g., area under the concentration-time profile (AUC)/minimum inhibitory concentration (MIC), percentage of dosing interval during which concentration is above MIC (% T>MIC), etc., to characterize outcomes. Conventional modeling methods typically take a snapshot of microbial burden at the end of an observation period and curve-fit the observations as a function of the surrogate index without making use of information at intermediate stages of the observation period (6-9, 13-16). Not surprisingly, these modeling approaches have restricted predictive ability and their limitations have been reviewed previously (17). On the other hand, modeling methods that make use of all available information on microbial burden during an observation period offer distinct advantages, in terms of being capable of accounting for the selective pressure that an antimicrobial agent exerts on a microbial population and to make useful predictions of microbial response to antimicrobial agents (18).

The ability to predict microbial response to antimicrobial agents is of great importance in the efforts in combating antimicrobial resistance. If the most effective dosing regimen of an antimicrobial agent can be identified and used clinically, it is hoped that the emergence of antimicrobial resistance can be suppressed (or delayed). Mathematical modeling and computer simulation of microbial response to antimicrobial agents hold great promise in accelerating and improving the development of antimicrobial agents. They have the capability to perform comprehensive screening of a large number of agent candidates to guide highly targeted testing. Thus, only promising agents and dosing regimens with high probabilities of success need be investigated subsequently in (pre-) clinical studies.

Thus, there is a significant need in the art for improvements in the area of high throughput screening for antimicrobial dosing regimens to increase the efficiency and cost-effectiveness of drug development. Specifically, the present invention is deficient in systems and methods of computer-implemented mathematical modeling useful to predict microbial response to a large number of antimicrobial agent and to design dosing regimens therefrom. The present invention fulfills this long-standing need and desire in the art.

SUMMARY OF THE INVENTION

The present invention is directed to a method of predicting the likelihood of a population of cells associated with a pathophysiological condition acquiring resistance to a therapeutic agent. The method comprises providing a computer-implemented simulation including at least an input/output system and a mathematical model of growth response over a period of time of a cell population in contact with a therapeutic agent. Parameter values are inputted into the mathematical model to determine at least cellular susceptibility to the antimicrobial agent and growth of a cell population during contact therewith over the period of time and output values are generated predicting cellular susceptibility and cell growth at incremental points over the time period. A decrease in cellular susceptibility output values and an increase in cell population growth values in a cell population which initially demonstrated susceptibility to the therapeutic agent at or near the end of the time period are correlated with a likelihood of acquisition of resistance of the cell population to the therapeutic agent.

The present invention is directed to a related method further comprising designing a dosing regimen that is pharmacologically effective against a cell population based on the output values over the time period of the mathematical model. The present invention is directed to another related method further comprising treating or preventing in a subject a pathophysiological condition caused by the cell population using the designed dosing regimen. The present invention is directed to yet another related method further, comprising compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations. The present invention is directed to still another related method further comprising screening a potential therapeutic agent for efficacy in suppressing resistance acquisition in one or more cell populations using the computer-implemented simulation.

The present invention also is directed to method for suppressing emergence of acquired resistance of a cell population to an therapeutic agent useful for treating a pathophysiological condition associated therewith in a subject. The method comprises administering to the subject a pharmacologically effective amount of an therapeutic compound on a dosing regimen determined via a computer simulation of growth response over a period of time of a cell population in contact with the therapeutic agent. The computer simulation is adapted to input parameter values into a mathematical model to determine at least cellular susceptibility to the therapeutic agent and growth of a cell population during contact therewith over the period of time and to generate output values predicting cellular susceptibility and cell growth at incremental points over the time period.

The present invention is directed further to method for high-throughput screening for therapeutic agents effective to suppress emergence of acquired resistance thereto in a cell population associated with a pathophysiological condition. The method comprises inputting initial parameter values into a computer-implemented simulation adapted to use a mathematical model which includes equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of burden in a surviving cell population. The equations are operably linked to the initial parameter values corresponding to time, infusion rate of the therapeutic agent, volume of distribution, clearance of the therapeutic agent, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population. During the computer simulation output values are generated predicting cellular susceptibility and cell growth at incremental points over the time period and output values and a decrease in cell population growth values at or near the end of the time period is correlated with suppression of emergence of acquired resistance within the cell population to the therapeutic agent. The present invention is directed to a related method further comprising compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations.

The present invention is directed further still to a computer-implemented system for high-throughput screening for therapeutic agents effective to suppress emergence of acquired resistance thereto in a cell population associated with a pathophysiological condition. The computer-implemented system comprises means for simulating growth response over a period of time of a cell population in contact with the therapeutic agent. The means for simulating the growth response includes at least an input/output system; and a mathematical model comprising, as operably linked components, equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of burden in a surviving cell population and initial parameter values for the equations corresponding to time, infusion rate of the therapeutic agent, volume of distribution, clearance of the therapeutic, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population. The computer-implemented system also comprises means for inputting parameter values into the mathematical model to determine at least cellular susceptibility to the therapeutic agent and growth of a cell population during contact therewith over the period of time, means for generating output values predicting cellular susceptibility and cell growth at incremental points over the time period and means for correlating, at or near the end of the time period, a decrease in cellular susceptibility output values and an increase in cell population growth values in a cell population which initially demonstrated susceptibility to the antimicrobial agent with a likelihood of acquisition of resistance of the cell population to the therapeutic agent.

The present invention is directed to a related computer-implemented system comprising means for designing a dosing regimen that is pharmacologically effective against a cell population based on the output values over the time period of the mathematical model. The present invention is directed to a further related computer-implemented system comprising means for compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations.

Other and further aspects, features and advantages of the present invention will be apparent from the following description of the presently preferred embodiments of the invention. These embodiments are given for the purpose of disclosure.

BRIEF DESCRIPTIONS OF THE DRAWINGS

So that the matter in which the above-recited features, advantages and objects of the invention, as well as others which will become clear, are attained and can be understood in detail, more particular descriptions of the invention briefly summarized above may be had by reference to certain embodiments thereof which are illustrated in the appended drawings. These drawings form a part of the specification. It is to be noted, however, that the appended drawings illustrate preferred embodiments of the invention and therefore are not to be considered limiting in their scope.

FIG. 1 illustrates the behavior of Eq. (9) for an initial population of 10⁸ microbes. Monotonic decline, monotonic growth and decline followed by regrowth patterns. The parts of the curves below the t axis are for illustration purposes only, since they correspond to eliminated populations (N<1).

FIG. 2A-2D illustrate the distribution of kill rate over microbial populations with Kg=0.3. Initial distributions are constructed by considering uniformly spaced values of agent-induced kill rate ri (I=1, . . . , 49) between (r_(min), r_(max))=(0.5, 0.9) for FIG. 2A and (r_(min), r_(max)) for FIG. 2B. FIGS. 2C-2D show the evolution of distribution curves for populations without and with resistant subpopulations. Note the distribution remains approximately normal.

FIG. 3A-3F illustrate the results of a computer simulation using Eqs. (7)-(9) to predicting the behavior of two subpopulations of microbes. Linear regression is used to fit values of the parameters a and b using (simulated) data (dashed lines) over the first 24 hours. Subsequently, the fitted values of a and b are used in Eqs. (7)-(9) to make approximate predictions (thick lines) beyond 24 hours, up to time tmax.

FIG. 4 illustrates a periodically fluctuating antimicrobial agent profile. Injection points can be seen every 8 hours. Decline of agent concentration is due to agent wash-off according to pharmacokinetics.

FIG. 5 illustrates a pharmacokinetic simulation in the hollow fiber infection model. R²=0.973; C_(max)=57.2 mg/L; C_(min)=1.7 mg/L; T½=1.5 hours; T>MIC 100%; and C_(min)/MIC=1.7.

FIGS. 6A-6E compare computer-simulated and experimental bacterial response to various meropenem exposures. Doses were given every 8 hours for 5 days in all treatment regimens.

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment of the present invention there is provided a method of predicting likelihood of a population of cells associated with a pathophysiological condition acquiring resistance to a therapeutic agent, comprising providing a computer-implemented simulation including at least an input/output system and a mathematical model of growth response over a period of time of a cell population in contact with a therapeutic agent; inputting parameter values into the mathematical model to determine at least susceptibility of the cells to the therapeutic agent and growth of a cell population during contact therewith over the period of time; generating output values predicting cellular susceptibility and cellular growth at incremental points over the time period; and correlating, at or near the end of the time period, a decrease in cellular susceptibility output values and an increase in cell population growth values in a cell population which initially demonstrated susceptibility to the therapeutic agent with likelihood of acquisition of resistance of the cell population to the therapeutic agent.

In a further embodiment the method comprises designing a dosing regimen that is pharmacologically effective against the cell population based on the output values over the time period of the mathematical model. Further still the method comprises treating or preventing in a subject a pathophysiological condition caused by the cell population using the designed dosing regimen. An example of a pathophysiological condition is a nosocomial infection or a cancer.

In another further embodiment the method comprises compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations. In yet another further embodiment the method comprises screening a potential therapeutic agent for efficacy in suppressing resistance acquisition in one or more cell populations using the computer-implemented simulation.

In all embodiments of this invention the mathematical model may comprise, as operably linked components, equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of burden in a surviving cell population; and initial parameter values for the equations corresponding to time, infusion rate of the therapeutic agent, volume of distribution, clearance of the therapeutic agent, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population.

Also in all embodiments the cell population may be a microbial population of Grain negative bacteria, Gram positive bacteria, yeast, mold, mycobacteria, virus, or infectious agents used in bioterrorism. Representative examples of Gram negative bacteria are Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Representative examples of Gram positive bacteria are Streptococcus pneumoniae and Staphylococcus aureus. A representative example of a virus is HIV or avian influenza. A representative example of an infectious agent used in bioterrorism is Bacillus anthracis.

In another embodiment of the present invention there is provided a method for suppressing emergence of acquired resistance of a cell population to a therapeutic agent useful for treating a pathophysiological condition associated therewith in a subject, comprising administering to the subject a pharmacologically effective amount of a therapeutic agent on a dosing regimen determined via a computer simulation of growth response over a period of time of a cell population in contact with the therapeutic agent, said computer simulation adapted to input parameter values into a mathematical model to determine at least cell susceptibility to the therapeutic agent and growth of a cell population during contact therewith over the period of time and to generate output values predicting cellular susceptibility and cell growth at incremental points over the time period.

In this embodiment the computer simulation may include at least an input/output system and the mathematical model which comprises, as operably linked components, computer-implemented equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of cell burden in a surviving cell population; and initial parameter values for the equations corresponding to time, infusion rate of the therapeutic agent, volume of distribution, clearance of the therapeutic, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population.

Also in this embodiment the dosing regimen may be determined at least from the output values over the time period of the mathematical model. The pathophysiological conditions and cell populations are as described supra.

In yet another embodiment of the present invention there is provided method for high-throughput screening for therapeutic agents effective to suppress emergence of acquired resistance thereto in a cell population associated with a pathophysiological condition, comprising:

inputting initial parameter values into a computer-implemented simulation adapted to use a mathematical model comprising equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of cell burden in a surviving cell population, where the equations are operably linked to the initial parameter values corresponding to time, infusion rate of the therapeutic, volume of distribution, clearance of the therapeutic, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population; generating output values during the computer simulation predicting cellular susceptibility and cell growth at incremental points over the time period; and correlating, at or near the end of the time period, an increase in cellular susceptibility output values and a decrease in cell population growth values with suppression of emergence of acquired resistance within the cell population to the therapeutic agent.

Further to this embodiment the method comprises compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations. In both embodiments the cell population and pathophysiological condition may be as described supra.

In yet another embodiment of the present invention there is provided a computer-implemented system for high-throughput screening for therapeutic agents effective to suppress emergence of acquired resistance thereto in a cell population associated with a pathophysiological condition, comprising means for simulating growth response over a period of time of a cell population in contact with the therapeutic agent including at least: an input/output system; and a mathematical model comprising, as operably linked components: equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of burden in a surviving cell population; and initial parameter values for the equations corresponding to time, infusion rate of the therapeutic agent, volume of distribution, clearance of the therapeutic, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population; means for inputting parameter values into the mathematical model to determine at least cellular susceptibility to the therapeutic agent and growth of a cell population during contact therewith over the period of time; means for generating output values predicting cellular susceptibility and cell growth at incremental points over the time period; and means for correlating, at or near the end of the time period, a decrease in cellular susceptibility output values and an increase in cell population growth values in a cell population which initially demonstrated susceptibility to the therapeutic agent with a likelihood of acquisition of resistance of the cell population to the therapeutic agent.

Further to this embodiment the computer-implemented system may comprise means for designing a dosing regimen that is pharmacologically effective against a cell population based on the output values over the time period of the mathematical model. In another further embodiment the computer-implemented system may comprise means for compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations.

As used herein, the term “a” or “an” may mean one or more. As used herein in the claim(s), when used in conjunction with the word “comprising”, the words “a” or “an” may mean one or more than one. As used herein “another” or “other” may mean at least a second or more of the same or different claim element of components thereof.

As used herein, the term “contact” refers to any suitable method or means whereby an antimicrobial agent is brought into contact with a microbial population, i.e., one or more, including all, of the microorganisms comprising the population. In vitro or ex vivo this is achieved by exposing the microbial population or microorganisms comprising the same to the antimicrobial agent in a suitable medium. For in vivo applications, any known method of administration is suitable as described herein.

As used herein, the term “subject” refers to a mammal, preferably a human that is the recipient of a therapeutic or pharmacologically effective or prophylactic treatment for a pathophysiological condition associated with a microbial population.

As used herein, the term “therapeutic agent” refers to any known or potential antimicrobial compounds or any known or potential chemotherapeutic compounds effective to suppress acquisition of resistance in a microbial or tumor cell population.

As used herein, the term “cell population” refers to both a microbial population or a population of cancer cells comprising, for example, a tumor.

The following abbreviations are used herein: MEM: meropenem; PIP: piperacillin; CAZ: ceftazidime; IPM: imipenem; LVX: levofloxacin; TOB: tobramycin; ND: not determined; OprD−: porin deletion; Mex+: MexAB-OprM efflux pump over-expression.

Provided herein is a mathematical model and computer simulation approach useful as a high throughput screening tool to facilitate rational dosing design in antimicrobial agent development. The mathematical model uses data from limited standard time-kill studies as inputs to capture the relationship between drug concentrations and killing rate. In conjunction with other derived model parameter estimates, such as microbial growth rate and/or adaptation tendency, comprehensive screening of efficacy and propensity to suppress resistance of a large number of dosing regimens are possible via computer simulations.

The present invention provides a mathematical model comprising three parallel differential equations characterizing the rate of change of drug concentration, microbial susceptibility and microbial burden of the surviving population over time, respectively. The mathematical equations of the model predicting bacterial response to various drug exposures are as follows:

$\begin{matrix} {\frac{{C(t)}}{t} = {\frac{Ro}{V} - {{CL} \cdot {C(t)}}}} & {{Eq}.\mspace{14mu} (I)} \end{matrix}$

Equation (I) describes the rate of change of antimicrobial agent concentration over time. Parameters and constants of Equation (1) include C(t) for the concentration of antimicrobial agent at time t, R₀ for the drug infusion rate, V for the volume of distribution, and CL for the clearance.

$\begin{matrix} {\frac{{C_{50{keff}}(t)}}{t} = {C_{50k} \cdot \alpha \cdot \left\{ ^{- {\lbrack{{{c{(t)}} \cdot t}\; \tau}\rbrack}} \right\} \cdot \left\lbrack {{c(t)} \cdot \tau} \right\rbrack}} & {{Eq}.\mspace{14mu} ({II})} \end{matrix}$

Equation (II) describes the rate of change of bacterial susceptibility to antimicrobial agent over time. Parameters and constants of Equation (2) include C_(50keff) for susceptibility of bacteria to antimicrobial agent, C_(50 k) for concentration to achieve 50% of maximal kill rate for bacterial population, a for maximal adaptation, and τ for the adaptation rate function.

$\begin{matrix} {\frac{{N(t)}}{t} = {{K_{g} \cdot \left\lbrack {1 - \frac{N(t)}{N_{\max}}} \right\rbrack \cdot {N(t)}} - {\left\{ \frac{{C(t)}^{H} \cdot K_{k}}{{C(t)}^{H} + \left\lbrack {C_{50{keff}}(t)} \right\rbrack^{H}} \right\} \cdot {N(t)}}}} & {{Eq}.\mspace{14mu} ({III})} \end{matrix}$

Equation (III) describes the rate of change of bacterial population over time. Parameters and constants of Equation (3) include N(t) for concentration of bacterial population at time t, K_(g) is the growth rate constant for bacterial population, N_(max) for the maximum population size, K_(k) for the maximal kill rate constant for bacterial population, and H for the sigmoidicity constant for bacterial population.

Note that C_(50keff)(t)=C_(50 k)·{1+α[1−e^(−[c(t)·t·τ])]}. The general solution for any C(t) is:

$\frac{{C_{50{keff}}(t)}}{t} = {C_{50k} \cdot \alpha \cdot \left\{ ^{- {\lbrack{{{c{(t)}} \cdot t}\; \tau}\rbrack}} \right\} \cdot \left\{ {\left\lbrack {{c(t)} \cdot \tau} \right\rbrack + \left\lbrack {t \cdot \tau \cdot \frac{{C(t)}}{t}} \right\rbrack} \right\}}$

and when C(t) is constant, the solution of the above equation is:

$\frac{{C_{50{keff}}(t)}}{t} = {C_{50k} \cdot \alpha \cdot \left\{ ^{- {\lbrack{{{c{(t)}} \cdot t}\; \tau}\rbrack}} \right\} \cdot {\left\lbrack {{c(t)} \cdot \tau} \right\rbrack.}}$

Generally the computer simulation utilizing the mathematical model is useful to predict the long-term growth/killing of an entire microbial population exposed to an antimicrobial agent at clinically relevant concentration profiles in vitro and in vivo. This provides a computer-implemented antimicrobial agent screening methodology and system useful to guide targeted preclinical/clinical testing of antimicrobial agents, thereby resulting in significant acceleration of the antimicrobial agent development process.

Thus, the present invention provides a computer-implemented pharmacokinetic and/or screening simulation tool which includes at least an input/output system and the mathematical model described herein. As is known and standard in the art an input/output system provides a user interface between the user of the computer-implemented tool and a computer system comprising the necessary components to run the simulation, e.g., a simulation engine. One of ordinary skill in the art would easily be able to provide a computer system with the necessary components, hardware and software to implement the computer simulation.

The computer-implemented system is adapted for high-throughput screening for antimicrobial agents effective to suppress emergence of acquired resistance thereto in a microbial population. Generally, the computer-implemented system comprises means for simulating growth response over a period of time of a microbial population in contact with the antimicrobial agent which may be the computer simulation and mathematical model described herein, means for inputting initial parameter and constant values and means for generating output values, as described herein, and means for correlating the output values with a likelihood of acquisition of resistance within the microbial population. In addition, and optionally, the system may further comprise means for designing a pharmacologically effective dosing regimen for the microbial population. Furthermore, means for compiling a library of the antimicrobial agents and respective dosing regimens optionally is provided.

Particularly, the present invention provides a computer-implemented method to predict the likelihood of a microbial population acquiring resistance to an antimicrobial agent over time. A time period may be, but not limited to about 24 hours. During the computer simulation initial parameter values and constants which are operably linked to the three differential equations comprising the mathematical model are entered as input. The initial parameter values are useful to determine at least microbial susceptibility to the antimicrobial agent and growth of a microbial population during contact therewith over the period of time. Output values may be generated during the simulation predicting microbial susceptibility and microbial growth at incremental points over the time period. The likelihood of the microbial population acquiring resistance of to the antimicrobial agent is predicted based on a decrease in microbial susceptibility output values and an increase in microbial population growth output values in the microbial population which initially demonstrated susceptibility to the antimicrobial agent at or near the end of the time period which is a positive correlator with resistance acquisition. Furthermore, this method of predicting likelihood of acquiring resistance and the computer simulation and mathematical model may be adapted for methods for high-throughput screening for antimicrobial agents effective to suppress emergence of acquired resistance in a microbial population.

As such the present invention further provides for designing a dosing regimen that is pharmacologically effective against a microbial population based on the output values over the time period of the mathematical model. Furthermore, a library comprising the screened antimicrobial agents and the associated designed dosing regimens may be compiled.

In addition, the designed dosing regimens may be used to treat or prevent in a subject a pathophysiological condition caused by the microbial population for which the dosing regimen was designed. Routes of administration of an antimicrobial agent and pharmaceutical compositions, formulations and carriers thereof are standard and well-known in the art. They are routinely selected by one of ordinary skill in the art based on, inter alia, the type and status of the pathophysiological condition, whether administration is for therapeutic or prophylactic treatment, and the subject's medical and family history.

Also, provided herein is a particular method for suppressing emergence of acquired resistance of a microbial population to an antimicrobial agent useful for treating a pathophysiological condition associated therewith in a subject. A pharmacologically effective amount of an antimicrobial agent may be administered using a dosing regimen determined via the computer simulation tool described herein. The computer simulation is adapted to input parameter values into a mathematical model to determine at least microbial susceptibility to the antimicrobial agent and growth of a microbial population during contact therewith over the period of time and to generate output values predicting microbial susceptibility and microbial growth at incremental points over the time period.

Antimicrobial agents of the present invention may include antibacterials, antifungals and antivirals. It is contemplated that this model and methodology may be applied to pathogens, such as, but not limited to, Gram-positive bacteria, e.g., Streptococcus pneumoniae and Staphylococcus aureus, Gram-negative bacteria, e.g., Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa, yeast, molds, mycobacteria, viruses, e.g., HIV and avian influenza, and infectious agents implicated in bioterrorism, for example, but not limited to Bacillus anthracis. The pathophysiological conditions may be any such condition associated with or caused by a microbial population. Particularly, the pathophysiological condition may be a nosocomial infection.

It is contemplated that the systems and methods provided herein are also effective against a malignant pathophysiological condition, such as a cancer. As is well known in the art, a cancer comprises a proliferating population of malignant cells and, as with a microbial population associated with a pathophysiological condition, a tumor cell population in the absence of treatment will grow overtime, eventually killing a subject having the cancer. Medical treatment, in terms of antimicrobial or chemotherapeutics is intended to disrupt the natural growth rate or to induce killing of the microbial population or tumor cell population. The same set of problems, e.g., combinatorily large number of variables involved, trial and error approach in drug development, drug resistance upon sub-optimal treatment, etc., arises in designing a dosing regimen for antimicrobial or chemotherapeutic treatments. Thus, the computer simulation and mathematical model provided herein may be applied to chemotherapeutic agents or potential chemotherapeutic agents in the treatment of cancer.

The following example(s) are given for the purpose of illustrating various embodiments of the invention and are not meant to limit the present invention in any fashion.

Example 1 PD of Heterogeneous Microbial Populations Under Constant Antimicrobial Concentration

In Equation (1):

$\begin{matrix} {{\frac{N}{t} = {\underset{{growth}\mspace{14mu} {rate}}{\underset{physiological}{\underset{}{K_{g}{N(t)}}}} - \underset{{due}\mspace{14mu} {to}\mspace{14mu} {agent}}{\underset{{kill}\mspace{14mu} {rate}}{\underset{}{{r\left( {C(t)} \right)}{N(t)}}}}}},{{N(0)} = N_{0}}} & {{Eq}.\mspace{14mu} (1)} \end{matrix}$

where N(t) is the total number of bacteria at time t; Kg is the physiological growth rate per unit of the bacteria (net effect of natural bacterial growth and death); and r(C) is the antimicrobial agent-induced kill rate per unit of bacteria, which is a non-decreasing function of the antimicrobial agent concentration C, usually expressed by the standard equation:

$\begin{matrix} {{r(C)} = {K_{k}\frac{C^{H}}{C^{H} + C_{50}^{H}}}} & {{Eq}.\mspace{14mu} (2)} \end{matrix}$

where K_(k) is the maximum kill rate; C₅₀ is the concentration at which 50% of the maximum kill rate is attained; and H is the Hill factor, indicating whether r(C) is heavily inflected (H>>1) or not.

Time-Kill Studies: Effect of Fixed Concentration

The following analysis is general and is not dependent on the particular functional form of r(C). For a time-invariant concentration C the standard solution of Equation 17 is

$\begin{matrix} {{{\ln \frac{N(t)}{N_{0}}} = {\alpha \; t}},} & {{Eq}.\mspace{14mu} (3)} \end{matrix}$

where α=K_(g)−r(C), implying linear dependence of log(N(t)) on t (straight line in a semi-logarithmic plot of N(t) vs. t).

Based on Equation 19 it is clear that the microbial population will tend to grow monotonically when

K _(g) −r(C)>0   Eq. (4)

whereas for

K _(g) −r(C)<0   Eq. (5)

the population will decline monotonically to zero. When K_(g)−r(C)=0 the microbial population will neither grow nor decline but will remain constant. The antimicrobial agent concentration that achieves this is the routinely and widely used minimal inhibitory concentration (MIC) (Craig, 1998; Mueller et al., 2004). Therefore, the value of r(C) in comparison with Kg represents the resistance of microbes to a specific antimicrobial agent at concentration C.

In reality, a growing population will eventually reach a saturation value N_(max) described by the logistic growth equation (Robertson, 1923):

$\begin{matrix} {{\frac{N}{t} = {\underset{{physiological}\mspace{14mu} {growth}\mspace{14mu} {rate}}{\underset{}{K_{g}{{N(t)}\left\lbrack {1 - \frac{N(t)}{N_{\max}}} \right\rbrack}}} - \underset{{due}\mspace{14mu} {to}\mspace{14mu} {agent}}{\underset{{kill}\mspace{14mu} {rate}}{\underset{}{{r\left( {C(t)} \right)}{N(t)}}}}}},{{N(0)} = N_{0}}} & {{Eq}.\mspace{14mu} (6)} \end{matrix}$

While Equation (6) is generally more accurate than Equation 17, Equation 17 is used in the ensuing analysis because microbial populations under antimicrobial agent pressure that forces them well below the point of growth saturation (N>>N_(max)) are examined. In that range, Equation 17 is fairly accurate.

While Equation 19 can only describe exponentially growing or declining populations N(t), in reality log(N(t)) hardly ever depends linearly on time t. Most notably, initial decline of N(t) may be followed by later regrowth. Such behavior is due to the fact that bacterial populations are heterogeneous, with differing degrees of antimicrobial resistance corresponding to different kill rate constants r_(i)(C) at a given antimicrobial concentration C. It is customary to lump subpopulations with r_(i)(C)<Kg into one resistant subpopulation and the remaining subpopulations, with r_(i)(C)≧Kg into one susceptible subpopulation. Equation 17 is then applied to each subpopulation. While this approach is conceptually appealing and computationally simple, it is only a rough approximation of the real system and may fail to predict phenomena such as regrowth.

To account for this, a mathematical modeling approach to the effect of antimicrobial agents on heterogeneous microbial populations is developed. This approach considers that there is a virtual continuum of kill rates r(C) distributed over a microbial population, because the size of such a population in an infection is of the order of. Then, one can prove the following, under fairly mild assumptions

The average kill rate is

$\begin{matrix} {{\mu (t)} \approx {\underset{b}{\underset{}{{\mu (0)} - \frac{{\sigma (0)}^{2}}{A}}} + {\underset{R}{\underset{}{\frac{{\sigma (0)}^{2}}{A}}}^{- {At}}}} \equiv {{Re}^{- {At}} + b}} & {{Eq}.\mspace{14mu} (7)} \end{matrix}$

the variance of kill rate is

σ(t)²≈σ(0)²e^(−At)   Eq. (8)

and, the total population is

$\begin{matrix} {{\ln \left\lbrack \frac{N(t)}{N_{0}} \right\rbrack} \approx {{\underset{K_{g} - b}{\underset{}{\left( {K_{g} - {\mu (0)} + \frac{{\sigma (0)}^{2}}{A}} \right)}}t} + {\underset{\frac{R}{A}}{\underset{}{\frac{{\sigma (0)}^{2}}{A^{2}}}}\left( {^{- {At}} - 1} \right)}}} & {{Eq}.\mspace{14mu} (9)} \end{matrix}$

where b is kill rate for most resistant subpopulation. To avoid regrowth, Kg−b<0, i.e. kill rate b is less than growth rate, K_(g) assumed to be the same for all bacteria.

Equation (9) can reproduce the effect of an antimicrobial agent on a microbial population, i.e., growth, decline and initial growth followed by regrowth. In addition it provides a very simple quantitative and qualitative explanation of the shape of experimentally observed curves of log (N(t)) vs. t, before the onset of saturation. Such curves can be approximated by a quadratic when r(C) is not uniform across the population, i.e., σ² is not equal to zero. The curvature of the quadratic is higher when σ² is larger, i.e., when resistance is more widespread over the population. In the special case of a homogeneous population, σ²=0 and Equations (8) and (9) reduce to Equation (3).

FIG. 1 illustrates these situations for an initial population of N₀=10⁸ microbes with a given resistance spread, treated by an antimicrobial agent at increasing concentrations, for which the initial average kill rate ranges from μ(0)<Kg (exponential growth) to μ(0)>>Kg (elimination of the entire population). To avoid population growth, one must select an agent concentration equal to the MIC of the most resistant subpopulation. But this will cause all other subpopulations (likely to constitute the majority of the overall population) to decline, thus causing reduction of the entire population rather than constancy, as usually required by the definition of MIC.

FIGS. 2A-2D illustrate computer simulation case of microbial populations with Kg=0.3 for each case. In both cases, the population consists of 49 subpopulations with kill rates distributed as shown in FIGS. 2A-2B. Each subpopulation grows or declines according to Eq. (1). Because each subpopulation grows or declines at a different rate, the kill rate distribution evolves with time towards smaller values, as shown in FIGS. 2C-2D. The kill rate distribution remains approximately normal for a certain period of time, as the kill rate average moves towards smaller values. The important difference between these two cases is that all subpoplation of the population in the first case correspond to kill rates r_(i) larger than Kg (FIG. 2A). Consequently, the entire population in the first case will be eliminated. By contrast, a tiny fraction of subpopulations in the population of the second case corresponds to kill rates r_(i) smaller than Kg (FIG. 2B) and will grow, thus causing the entire second population to eventually regrow.

FIGS. 3A-3F affirms that this behavior can be predicted by collecting data over a typical 24 hour period. The dashed lines in FIGS. 3A-3B show the total population, average kill rate and kill rate variance for each of the two populations. Assuming that data are available for the first 24 hours, linear regression is used to fit values of the parameters a, where a=Kg, and b, where b=1/2σ², into Equation 9. Subsequently, Equations 7-9 are used to make predictions beyond 24 hours for each population. The predictions are verified in FIG. 3A, which shows that the entire population in the first case is completely eliminated around 70 hours and in FIG. 3B, which shows that the population in the second case experiences regrowth around 35 hours, at which point μ(t)≈Kg. As expected, as time progresses, Eqns. (8) and (9) become less accurate.

Example 2 PD of Heterogeneous Microbial Populations Under PK-Realistic Antimicrobial Concentration

Assume now that the antimicrobial agent concentration does not remain constant, but fluctuates periodically (due to periodic injections) as shown, for example, in FIG. 1. If the fluctuations have period T, then

C(t)=C(t−kT)   Eq. (10)

where the integer k indicates the integer part of the real number t/T, denoted as

$\left\lbrack \frac{t}{T} \right\rbrack.$

The kill rate r(C(t)) corresponding to the above agent concentration will satisfy a similar periodic relationship as in Equation 23.

Under these conditions it can be shown that the total population N(t) exhibits periodic patterns with period T, and the values of N(nt) follow an equation similar to that for the constant-concentration case, as made precise by the following.

For homogeneous population dynamics under periodically fluctuating agent concentration, assume that a homogeneous population of N₀ bacteria satisfying Equation 17 is subjected to periodically fluctuating antimicrobial agent concentration C(t) satisfying Equation 23. Then the bacterial population is

$\begin{matrix} {{\ln \left\lbrack \frac{N(t)}{N_{0}} \right\rbrack} = {{K_{g}t} - {\left\lbrack \frac{t}{T} \right\rbrack {DT}} - {\int_{0}^{t - {{\lbrack\frac{1}{T}\rbrack}T}}{{r\left( {C(\eta)} \right)}\ {\eta}}}}} & {{Eq}.\mspace{14mu} (11)} \end{matrix}$

where

$\left\lbrack \frac{t}{T} \right\rbrack$

indicates the integer part of the real number t/T, and

$\begin{matrix} {D\hat{=}{\frac{1}{T}{\int_{0}^{T}{{r\left( {C(\eta)} \right)}\ {\eta}}}}} & {{Eq}.\mspace{14mu} (12)} \end{matrix}$

is the time-averaged kill rate; and at times t=nT , n=0, 1, 2, . . . the total population satisfies the equation

$\begin{matrix} {{{\ln \left\lbrack \frac{N({nT})}{N_{0}} \right\rbrack} = {\left( {K_{g} - D} \right){nT}}},{n = 0},1,2,{\ldots \mspace{14mu}.}} & {{Eq}.\mspace{14mu} (13)} \end{matrix}$

Similarly to Equation 19 (constant agent concentration), Equation 26 (periodic agent concentration) indicates that the points

$\frac{N({nT})}{N_{0}},$

n=0, 1, 2, . . . in a logarithmic plot will lie on a straight line corresponding to the apparent kill rate D in Equation 25. In other words, the points

$\frac{N({nT})}{N_{0}}$

appear as if they were generated by a system under constant agent concentration D. All that is required for prediction of population growth or decline is to know the size of D in comparison to K_(g), e.g. the value of

$\frac{D}{K_{g}}$

in comparison to 1.

If the agent concentration follows the pharmacokinetic pattern:

C(t)=C _(max) e ^(−t/T), 0≦t<T   Eq. (14)

(FIG. 4) where

$\tau = \frac{t_{1/2}}{\ln \; 2}$

is the pharmacokinetic time constant (proportional to half-time) and T is the injection interval, then the value of

$\frac{D}{K_{g}}$

can be influenced by selecting two dimensionless variables associated with the dose and injection interval of a dosing regimen:

$z\hat{=}\frac{C_{avg}}{MIC}$

(or, equivalently,

${y\hat{=}\frac{C_{avg}}{C_{50}}}{\mspace{14mu} \mspace{14mu}}$

and

${x\hat{=}\frac{T}{\tau}},$

where

$\begin{matrix} {C_{avg}\hat{=}{\frac{1}{T}{\int_{0}^{T}{{C(\eta)}{\eta}}}}} & {{Eq}.\mspace{14mu} (15)} \end{matrix}$

is proportional to the administered dose (mass over 24 hour period).

Also, the functional dependence of

$\frac{D}{K_{g}}$

on x, z depends on two pharmacodynamic parameters, H and

$\frac{K_{k}}{K_{g}}.$

Under the assumptions in eqns. 1, 2, and 14,

$\begin{matrix} \begin{matrix} {\frac{D}{K_{g}} = {\frac{K_{k}}{K_{g}}\frac{1}{Hx}\ln \frac{\frac{K_{k}}{K_{g}} - 1 + \left( \frac{^{x}{xz}}{^{x} - 1} \right)^{H}}{\frac{K_{k}}{K_{g}} - 1 + \left( \frac{xx}{^{x} - 1} \right)^{H}}}} \\ {= {\left( {1 + \chi_{50}^{H}} \right)\frac{1}{Hx}\ln \frac{1 + \left( \frac{^{x}{xy}}{^{x} - 1} \right)^{H}}{1 + \left( \frac{xy}{^{x} - 1} \right)^{H}}}} \end{matrix} & {{Eq}.\mspace{14mu} (16)} \end{matrix}$

where

${\chi_{50}^{H}\hat{=}{\left( \frac{C_{50}}{MIC} \right)^{H} = {\frac{K_{k}}{K_{g}} - 1}}},{z\hat{=}\frac{C_{avg}}{MIC}},{y\hat{=}\frac{C_{avg}}{C_{50}}},\mspace{14mu} {and}$ ${x\hat{=}\frac{T}{\tau}},$

with MIC defined as K_(g)−r(MIC)=0. Also

${\frac{D}{K_{g}} = {\left( {1 + {\chi 50}^{H}} \right)\frac{1}{Hx}\ln \frac{1 + \left( \frac{^{x}{xy}}{^{x} - 1} \right)^{H}}{1 + \left( \frac{xy}{^{x} - 1} \right)^{H}}}}\;$ where ${x = \frac{r}{\tau}},{y = \frac{c_{avg}}{c\; 50}},\; {{\chi 50}^{H} = {\left( \frac{c\; 5\; 0}{MIC} \right)^{H} = \frac{K_{k} - K_{g}}{K_{g}}}},{{c(f)} = {c_{\max \mspace{11mu}}\; ^{{- t}/\tau}}},\; {0 \leq t < T}$

Equation (16) also can be used with homogeneous population dynamics under periodically fluctuating agent concentration where T is the dosing interval, and MIC is defined as K_(g)−r(MIC)=0. Also:

${{\chi_{50}^{H}\hat{=}{\left( \frac{C_{50}}{MIC} \right)^{H} = {\frac{K_{k}}{K_{g}} - 1}}},\; {z\hat{=}{\frac{C_{avg}}{MIC} = {\frac{{daily}\mspace{14mu} {{dose}\mspace{14mu}\lbrack{mg}\rbrack}}{{24\mspace{14mu}\lbrack{hours}\rbrack}\mspace{14mu} {{clearance}\mspace{14mu}\left\lbrack {L/{hour}} \right\rbrack}}/{MIC}}}},\; {y\hat{=}{\frac{C_{avg}}{C_{50}} = {\frac{{daily}\mspace{14mu} {{dose}\mspace{14mu}\lbrack{mg}\rbrack}}{{24\mspace{14mu}\lbrack{hours}\rbrack}\mspace{14mu} {{clearance}\mspace{14mu}\left\lbrack {L/{hour}} \right\rbrack}}/C_{50}}}},{{{and}\mspace{14mu} x}\hat{=}{\frac{T}{\tau} = {\frac{T}{t_{1/2}}\ln \; 2.}}}}\;$

For homogeneous population dynamics under periodically fluctuating agent concentration, assume that a homogeneous population of N₀ bacteria satisfying Equation 17 is subjected to periodically fluctuating antimicrobial agent concentration C(t) satisfying Equation 23. Then the bacterial population is

$\begin{matrix} {{\ln \left\lbrack \frac{N(t)}{N_{0}} \right\rbrack} = {{K_{g}t} - {\left\lbrack \frac{t}{T} \right\rbrack {DT}} - {\int_{0}^{t - {{\lbrack\frac{t}{T}\rbrack}{T.}}}{{r\left( {C(\eta)} \right)}{\eta}}}}} & {{Eq}.\mspace{14mu} (17)} \end{matrix}$

where

$\left\lbrack \frac{t}{T} \right\rbrack$

where indicates the integer part of the real number t/T, and

$\begin{matrix} {D\hat{=}{\frac{1}{T}{\int_{0}^{T}{{r\left( {C(\eta)} \right)}{\; \eta}}}}} & {{Eq}.\mspace{14mu} (18)} \end{matrix}$

is the time-averaged kill rate; and at times t=nT, n=0, 1, 2, . . . the total population satisfies the equation

$\begin{matrix} {{{\ln \left\lbrack \frac{N\left( {n\; T} \right)}{N_{0}} \right\rbrack} = {\left( {K_{g} - D} \right)n\; T}},\mspace{14mu} {n = 0},1,2,\ldots} & {{Eq}.\mspace{14mu} (19)} \end{matrix}$

Similarly to Equation 26 (constant agent concentration), eqn. 19 (periodic agent concentration) indicates that the points

$\frac{N\left( {n\; T} \right)}{N_{0}},$

n=0, 1, 2, . . . in a logarithmic plot will lie on a straight line corresponding to the apparent kill rate D in eqn. (18). In other words, the points

$\frac{N\left( {n\; T} \right)}{N_{0}}$

appear as if they were generated by a system under constant agent concentration D. What is required for prediction of population growth or decline is to know the size of D in comparison to K_(g), e.g. the value of

$\frac{D}{K_{g}}$

comparison to 1. If

${\frac{D}{K_{g}} < 1},$

prevention of population regrowth is guaranteed. If

${\frac{D}{K_{g}} \geq 1},$

unless the entire population is eliminated within one period.

Example 3 Antimicrobial Agents

Meropenem powder was supplied by AstraZeneca (Wilmington, Del.). A stock solution of the drug (1024 μg/ml) in sterile water was prepared, aliquoted, and stored at −70° C. Prior to each susceptibility testing, an aliquot of the drug was thawed and diluted to the desired concentrations with cation-adjusted Mueller-Hinton broth (Ca-MHB) (BBL, Sparks, Md.).

Microorganisms

P. aeruginosa ATCC 27853 (American Type Culture Collection, Rockville, Md.) was used in the study. The bacteria were stored at −70° C. in Protect® (Key scientific products, Round Rock, Tex.) storage vials. Fresh isolates were sub-cultured twice on 5% blood agar plates (Hardy Diagnostics, Santa Maria, Calif.) for 24 hours at 35° C. prior to each experiment.

Susceptibility Studies

Meropenem minimum inhibitory concentration (MEC)/minimum bactericidal concentration (MBC) were determined in Ca-MHB using a macrobroth dilution method as described by the CLSI (formerly National Committee for Clinical Laboratory Standards) (10). The final concentration of bacteria in each macrobroth dilution tube was approximately 5×10⁵ cfu/ml of Ca-MHB. Serial twofold dilutions of meropenem were: used. The minimum inhibitory concentration was defined as the lowest concentration of drug that resulted in no visible growth after 24 hours of incubation at 35° C. in ambient air. Samples (50 μl) from clear tubes and the cloudy tube with the highest drug concentration were plated on Mueller-Hinton agar (MHA) plates (Hardy Diagnostics, Santa Maria, Calif.). The minimum bactericidal concentration was defined as the lowest concentration of drug that resulted in ≧99.9% kill of the initial inoculum. Drug carry-over effect was assessed by visual inspection of the distribution of colonies on media plates. The studies were conducted in duplicate and repeated at least once on a separate day. The minimum inhibitory concentration and minimum bactericidal concentration of the standard isolate to meropenem are each 1 μg/ml, respectively.

Time-Kill Studies

Time-kill studies data over 24 hours have been reported previously (21). A clinically relevant (achievable) concentration range of meropenem (0-64 μg/ml) was used. A dense baseline inoculum (approximately 2×10⁸ cfu/ml) was used to simulate the bacterial load in severe nosocomial infections. The data were used as inputs to derive the best-fit model parameter estimates, as previously described. Overall, the observed bacterial burdens over time (under constant antimicrobial agent concentrations) were reasonably described and predicted by the mathematical model.

Pharmacokinetic Profiles

Different dosing strategies of meropenem were investigated for their propensity to suppress resistance. A fixed maximum concentration (Cmax) resulting from a 1 g clinical dose (64 μg/ml) with repeated dosing every 8 hours (to re-attain Cmax) was used in all the dosing regimens investigated. The dosing regimens differed in the simulated elimination half-lives (1-3 hours), resulting in different concentrations at the end of the dosing interval (Cmin).

Computer Simulations

Using the best-fit model parameter values derived, microbial response to various meropenem exposures over 5 days was predicted. The three parallel differential equations described herein are used, each characterizing the rate of change of drug concentration (pharmacokinetics), microbial susceptibility and microbial burden of the surviving population over time, respectively. All simulations were performed with the ADAPT II program (19).

Hollow-Fiber Infection Model

The computer simulations were compared to experimental data from an in-vitro hollow fiber infection model with similar antimicrobial agent exposures (20). Basically meropenem was injected directly into the central reservoir to achieve the peak concentration desired. Fresh (drug-free) growth medium (Ca-MHB) was continuously infused from the diluent reservoir into the central reservoir to dilute the drug in order to simulate drug elimination in humans. An equal volume of meropenem-containing medium was removed from the central reservoir concurrently to maintain an isovolumetric system. Bacteria were inoculated into the extracapillary compartment of the hollow-fiber cartridge (Fibercell Systems, Inc., Frederick, Md.); they are confined in the extracapillary compartment but are exposed to the fluctuating drug concentration in the central reservoir by means of an internal circulatory pump in the bioreactor loop. The optional absorption compartment of the system was not used. Various elimination half-lives (1-3 hours) of meropenem were simulated and were validated subsequently in the infection models. Serial samples were obtained at baseline and daily (pre-dose) in duplicate from each hollow fiber system, for quantitative culture to define the effect of various drug exposures on the total bacterial population and on selection of resistant bacterial sub-populations.

Prior to culturing the bacteria quantitatively, the bacterial samples were centrifuged at 15,000 G for 15 minutes and reconstituted with sterile normal saline in order to minimize drug carry-over effect. Total bacterial populations were quantified by spiral plating 10× serial dilutions of the samples (50 μl) onto drug-free Mueller-Hinton agar (MHA) plates (Hardy Diagnostics, Santa Maria, Calif.). Sub-populations with reduced susceptibility (resistant) were quantified by culturing onto MHA plates supplemented with meropenem at a concentration of 3× MIC of meropenem. Since susceptibility testing is performed in twofold dilutions and 1 tube (2× in concentration) difference is commonly accepted as reasonable interday variation, quantitative cultures on drug supplemented media plates (at 3× MIC) would allow reliable detection of bacterial sub-populations with reduced susceptibility. The media plates were incubated at 35° C. for up to 24 (total population) and 72 hours (sub-populations with reduced susceptibility), then bacterial density from each sample was estimated by CASBA-4 colony scanner/counter (Spiral Biotech, Bethesda, Md.). The theoretical lower limit of detection was 400 cfu/ml.

Mechanism(s) of Resistance

In order to substantiate that bacterial regrowth over time was due to emergence of resistance, the susceptibility of the resistant isolates (recovered from the drug-supplemented media plates at the end of the experiments) to meropenem was repeated. The susceptibility of these meropenem-resistant isolates to a screening panel of antimicrobial agents was also performed to provide insights on the likely mechanism(s) of resistance. Based on the phenotypic resistance profiles of the resistant isolates, the mechanism(s) of resistance was investigated using an appropriate and well-known methodology, i.e., SDS-PAGE and/or Western immunoblotting with anti-MexB antibodies, as described previously (20).

Example 4 Comparison of Computer Simulations and Experimental Validation

The pharmacokinetic simulation in the hollow fiber infection model was satisfactory (FIG. 5). The comparison between computer simulated and experimental microbial responses are as shown in FIGS. 6A-6E. Overall, the computer predictions correlated well with experimental data qualitatively with respect to eradication or regrowth due to resistance emergence. A significant initial reduction in microbial burden was predicted for all dosing regimens examined. However, regrowth over time was predicted for sub-optimal regimens with repeated dosing due to selective amplification of resistant sub-population(s). On the other hand, sustained suppression of resistance emergence was achieved with optimal dosing regimens. Using the conventional nomenclature, the computer simulations were correct in predicting that all dosing regimens with % T>MIC of <100% were associated with regrowth. In order to suppress the emergence of resistance, dosing regimens achieving Cmin/MIC≧4 would be necessary.

Example 5

Susceptibility Profiles of Resistant P. aeruginosa Isolates

The susceptibility profiles of the resistant isolates recovered from MEM-supplemented plates are shown in Table 1. Meropenem resistance was phenotypically stable. The mechanisms, of resistance were found to be deletion of outer membrane porin (OprD) protein and efflux pump (MexAB-OprM) over-expression, as reported previously (20). These data provided further molecular evidence on the emergence of resistance over time, consistent with our modeling strategy to account for regrowth.

TABLE 1 Exposure MEM* Mechanism(s) Strain (C_(min)/MIC) (mg/L) PIP CAZ IPM LVX TOB CAR* of resistance PA — 1 3 1 3 0.75 0.5  64 — 27853 MR1 Placebo 4 3 1 >32 0.75 0.5 ND OprD− MR2 0.5 64 8 2 >32 4 0.5 512 OprD− and Mex+ MR3 1.7 32 32 4 326 0.5 512 OprD− and Mex+ *Meropenem and carbenicillin MIC determined by macrobroth method, other MIC determined by Etest

Example 6 Predictive Performance of the Mathematical Model in an In Vivo Animal Model Murine Pneumonia Model

22-28 gram, female Swiss Webster mice are housed in isolation boxes to decrease the risk of infection from extraneous pathogens. The mice are allowed to eat and to drink ad libitum. In order to mimic the human pharmacokinetic profiles, nephrotoxicity is induced by an intraperitoneal (IP) injection of uranyl nitrate 5 mg/kg 1 day prior to infection. This procedure has been reported to result in transient renal failure that persists for 5-6 days after the injection (21).

The inoculum required to establish a persistent and reproducible infection in non-neutropenic mice is determined for each bacterial isolate. The bacteria is inoculated into the trachea of the anesthetized mice under laryngoscopic guidance. Antimicrobial treatment is given intraperitoneally 2 hours after infection for 96 hours. Dosing regimens are based on prior pharmacokinetic studies in infected mice and antimicrobial agent exposure necessary to suppress resistance emergence as determined supra. Groups of mice are sacrificed daily and total and resistant bacterial burden in lung tissues is determined.

Determining an Optimal Inoculum for Immunocompetent Mice

An optical inoculum resulting in persistent infection in immunocompetent mice without an excessively high mortality, i.e., mortality is less than 50%, is determined. The inoculum is prepared as described in Example 1. Sixty mice are divided randomly into 4 groups. Each animal is infected with an inoculum ranging from 1×10⁷ to 5×10⁸ CFU. The infected mice are examined every 12 hours for 4 days. Moribund mice are sacrificed humanely at each inspection time and death is recorded as occurring in the next inspection time. All surviving mice are sacrificed at 96 hours after infection. Lungs from each mouse are collected aseptically upon death or at the end of the experiment. The lung tissues are homogenized and diluted 10-fold serially in sterile saline. Total and resistant bacterial populations are quantified at all time points as described in Example 1. This study can be repeated with other strains of bacteria.

Pharmacokinetics in Infected Mice

Fifty-four mice are infected with the determined optimal inoculum. The mice are divided randomly into 3 groups (18 mice/group). Two hours after bacterial inoculation, all the mice in each group are injected with an intraperitoneal dose of an anti-pseudomonal agent, e.g., meropenem. Three incremental dosages are used for the 3 groups, i.e., 25 mg/kg, 100 mg/kg and 400 mg/kg). At 0.25, 0.5, 1, 2, 4, 8 hours after the injection, three mice per group are sacrificed and blood is collected via cardiac puncture. The blood is allowed to clot on ice and the serum is collected. The antimicrobial agent concentration in each sample is assayed by a validated HPLC methodology, as previously described (22-23).

The pharmacokinetics of the antimicrobial agent in infected mice is analyzed by a population pharmacokinetic analysis and the best-fit pharmacokinetic model parameter estimates are used to determine the appropriate dosage necessary to achieve a human clinical dose exposure associated with resistance suppression (3, 12, 17, 20, 24-25). Population pharmacokinetic analysis is preferred over the conventional two-stage method as it has been shown to be more precise in characterizing drug disposition and predicting clinical achievable drug exposure (26-28). Using the dose associated with the optimal human clinical dose exposure, this sampling procedure is repeated with infected mice on day 2, 3 and 4 after infection to determine if the simulated human antimicrobial agent clearance could be sustained for more than 24 hours. Serum protein binding of the antimicrobial agents will be assessed by the above analytical method with and without ultrafiltration.

Validation of the In-Vivo Impact of Antimicrobial Agent Dosing Regimens on P. aeruginosa Resistance Suppression

Based on the established optimal experimental conditions, 52 infected mice are treated with placebo, e.g., sterile saline or an anti-pseudomonal agent, e.g., meropenem. The mice are divided into 3 groups, each group treated with a different and escalating, i.e., placebo, low and high, human-like dosing regimen 2 hours after infection. The specific dosing regimens to simulate are based on results from one sub-optimal human-like dosing regimen predicted to select resistance in P. aeruginosa and one optimal human-like dosing regimen predicted to suppress resistance development.

Four infected mice in the placebo group are sacrificed 2 hours after infection, i.e., immediately after placebo treatment, to ascertain the initial infective inoculum. The remaining 48 infected mice are treated for 96 hours. They are examined every 12 hours and moribund mice are sacrificed humanely at each inspection time. Four mice in each dosing group are sacrificed at 24, 48, 72 and 96 hours after the first dose. The bacterial load (total and resistant) in lung tissues of the infected mice is determined as described previously herein. The observations are compared to the predictions from the mathematical model. Other strains of bacteria may be used in this model and subsequent validation of its predictions.

The following references are cited herein:

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Any patents or publications mentioned in this specification are indicative of the levels of those skilled in the art to which the invention pertains. Further, these Patents and publications are incorporated by reference herein to the same extent as if each individual publication was specifically and individually incorporated by reference.

One skilled in the art will appreciate readily that the present invention is well adapted to carry out the objects and obtain the ends and advantages inherent herein. The present examples, along with the methods, procedures, systems, and/or applications described herein are presently representative of preferred embodiments, are exemplary and are not intended as limitations on the scope of the invention. Changes therein and other uses will occur to those skilled in the art which are encompassed within the spirit of the invention as defined by the scope of the claims. 

1. A method of predicting likelihood of a population of cells associated with a pathophysiological condition acquiring resistance to a therapeutic agent, comprising: providing a computer-implemented simulation including at least an input/output system and a mathematical model of growth response over a period of time of a cell population in contact with a therapeutic agent; inputting parameter values into the mathematical model to determine at least susceptibility of the cells to the therapeutic agent and growth of a cell population during contact therewith over the period of time; generating output values predicting cellular susceptibility and cellular growth at incremental points over the time period; and correlating, at or near the end of the time period, a decrease in cellular susceptibility output values and an increase in cell population growth values in a cell population which initially demonstrated susceptibility to the therapeutic agent with likelihood of acquisition of resistance of the cell population to the therapeutic agent.
 2. The method of claim 1, wherein the mathematical model comprises, as operably linked components: equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of burden in a surviving cell population; and initial parameter values for the equations corresponding to time, infusion rate of the therapeutic agent, volume of distribution, clearance of the therapeutic agent, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population.
 3. The method of claim 1, further comprising designing a dosing regimen that is pharmacologically effective against the cell population based on the output values over the time period of the mathematical model.
 4. The method of claim 3, further comprising treating or preventing in a subject the pathophysiological condition caused by the cell population using the designed dosing regimen.
 5. The method of claim 4, wherein the pathophysiological condition is a nosocomial infection or a cancer.
 6. The method of claim 1, wherein the cell population is a cell population of Gram negative bacteria, Gram positive bacteria, yeast, mold, mycobacteria, virus, or infectious agents used in bioterrorism.
 7. The method of claim 6 wherein the cell population is Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus pneumoniae, Staphylococcus aureus, HIV, avian influenza, or Bacillus anthracis.
 8. The method of claim 3, further comprising compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations.
 9. The method of claim 1, further comprising screening a potential therapeutic agent for efficacy in suppressing resistance acquisition in one or more cell populations using the computer-implemented simulation.
 10. A method for suppressing emergence of acquired resistance of a cell population to a therapeutic agent useful for treating a pathophysiological condition associated therewith in a subject, comprising: administering to the subject a pharmacologically effective amount of a therapeutic agent on a dosing regimen determined via a computer simulation of growth response over a period of time of a cell population in contact with the therapeutic agent, said computer simulation adapted to input parameter values into a mathematical model to determine at least cell susceptibility to the therapeutic agent and growth of a cell population during contact therewith over the period of time and to generate output values predicting cellular susceptibility and cell growth at incremental points over the time period.
 11. The method of claim 10, wherein the computer simulation includes at least an input/output system and the mathematical model which comprises, as operably linked components: computer-implemented equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of cell burden in a surviving cell population; and initial parameter values for the equations corresponding to time, infusion rate of the therapeutic agent, volume of distribution, clearance of the therapeutic, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population.
 12. The method of claim 10, wherein the dosing regimen is determined at least from the output values over the time period of the mathematical model.
 13. The method of claim 10, wherein the pathophysiological condition is a nosocomial infection or a cancer.
 14. The method of claim 13, wherein the cell population is a population of Gram negative bacteria, Gram positive bacteria, yeast, mold, mycobacteria, virus, or infectious agents used in bioterrorism.
 15. The method of claim 14 wherein the cell population is Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus pneumoniae, Staphylococcus aureus, HIV, avian influenza, or Bacillus anthracis.
 16. A method for high-throughput screening for therapeutic agents effective to suppress emergence of acquired resistance thereto in a cell population associated with a pathophysiological condition, comprising: inputting initial parameter values into a computer-implemented simulation adapted to use a mathematical model comprising: equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of cell burden in a surviving cell population, said equations operably linked to the initial parameter values corresponding to time, infusion rate of the therapeutic, volume of distribution, clearance of the therapeutic, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population; generating output values during the computer simulation predicting cellular susceptibility and cell growth at incremental points over the time period; and correlating, at or near the end of the time period, an increase in cellular susceptibility output values and a decrease in cell population growth values with suppression of emergence of acquired resistance within the cell population to the therapeutic agent.
 17. The method of claim 16, further comprising compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations.
 18. The method of claim 16, wherein the pathophysiological condition is a nosocomial infection or a cancer.
 19. The method of claim 16, wherein the cell population is a microbial population of Gram negative bacteria, Gram positive bacteria, yeast, mold, mycobacteria, virus, or infectious agents used in bioterrorism.
 20. The method of claim 19, wherein the microbial population is Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus pneumoniae, Staphylococcus aureus, HIV, avian influenza, or Bacillus anthracis.
 21. A computer-implemented system for high-throughput screening for therapeutic agents effective to suppress emergence of acquired resistance thereto in a cell population associated with a pathophysiological condition, comprising: means for simulating growth response over a period of time of a cell population in contact with the therapeutic agent including at least: an input/output system; and a mathematical model comprising, as operably linked components: equations for calculating in parallel and over a specified time period a rate of change of concentration of the therapeutic agent in the cell population, a rate of change of cellular susceptibility to the therapeutic agent and a rate of change of burden in a surviving cell population; and initial parameter values for the equations corresponding to time, infusion rate of the therapeutic agent, volume of distribution, clearance of the therapeutic, concentration to achieve 50% of maximal kill rate of a cell population, and maximum size of a cell population and constants for maximum adaptation and adaptation rate of a cell population and growth rate, maximum kill rate and sigmoidicity of a cell population; means for inputting parameter values into the mathematical model to determine at least cellular susceptibility to the therapeutic agent and growth of a cell population during contact therewith over the period of time; means for generating output values predicting cellular susceptibility and cell growth at incremental points over the time period; and means for correlating, at or near the end of the time period, a decrease in cellular susceptibility output values and an increase in cell population growth values in a cell population which initially demonstrated susceptibility to the therapeutic agent with a likelihood of acquisition of resistance of the cell population to the therapeutic agent.
 22. The computer-implemented system of claim 21, further comprising means for designing a dosing regimen that is pharmacologically effective against a cell population based on the output values over the time period of the mathematical model.
 23. The computer-implemented system of claim 22, further comprising means for compiling a library of therapeutic agents and dosing regimens effective to suppress the emergence of acquired resistance in cell populations.
 24. The method of claim 21, wherein the pathophysiological condition is a nosocomial infection or a cancer. 